Jonah Burian: Lessons from the Crypto Cycle Apply to AI Investment

Blockchain Capital investor Jonah Burian stated: Cryptocurrency accelerates market cycles, early companies gain liquidity through tokens, and market behavior is assumed to be public, with lessons applicable to AI investment. Huge results are contagious and trigger FOMO; after Bitcoin became a trillion-dollar asset, Ethereum and Solana proved that more significant outcomes are possible, leading to the rise of alt-L1 trading, with VCs treating new L1s as lottery funding. Similarly, OpenAI and Anthropic are moving towards the trillion-dollar scale, with each new lab being priced as a lottery.

L1s were financed at billion-dollar valuations based on white papers and founding teams, while new labs are raising billions based on research papers and teams poached from OpenAI, Anthropic, or Google DeepMind. After hot money floods in, rapid capital speculators always emerge; cryptocurrency has experienced operations like tokens as products, high FDV with low circulation, and a similar dynamic is unfolding in AI, but AI prices are formed in opaque semi-liquid secondary markets, unlike publicly traded cryptocurrencies.

The blockchain space has shifted from scarcity to abundance, becoming a commodity, with applications capturing most of the value. In AI, models may become commoditized, with value shifting upstream and downstream, while applications and hardware layers gain profits, and the model layer gets squeezed. During the frenzy phase, financial capital excessively funds infrastructure, and new labs bet that it is only reasonable to assume large-scale R&D returns, with the history of cryptocurrency and alt-L1s warranting skepticism unless AGI arrives.